Senior Application Development Lead – Generative AI (Gen AI), Vice President

Overview

In this role you will lead Gen AI initiatives within Citi’s Source to Pay Solutions Technology group. You will set technical direction for Gen AI adoption, architect reusable patterns, and drive end-to-end delivery of AI-enabled features in critical applications. You’ll collaborate with global teams to align with risk, regulatory, and transformation priorities while ensuring secure, scalable, and responsible AI implementations. This is a hands-on leadership role that shapes technology strategy and accelerates value realization through AI governance and platform engineering. You will work in a collaborative, hybrid environment with meaningful impact across the organization.

Pay / Benefits

• generous holiday allowance
• discretional annual bonus
• private medical insurance
• Employee Assistance Program
• pension plan
• paid parental leave

Responsibilities

• Set technical direction for Gen AI adoption across multiple applications or teams with reusable patterns and reference architectures
• Design and evolve Gen AI capabilities (LLMs, APIs, agentic workflows, automation) for value, reliability, and responsible use
• Own end-to-end delivery of Gen AI features from design to production rollout and continuous improvement
• Collaborate with global and regional technology and business teams to meet regulatory, risk, and transformation priorities
• Apply agile engineering practices and leverage emerging Gen AI tooling to accelerate outcomes
• Provide technical leadership through hands-on development, code reviews, and design guidance
• Ensure solutions meet Citi security, stability, resilience, and operational control standards
• Continuously assess system quality balancing performance, cost, and user experience in production Gen AI apps

Key requirements

• Deep expertise in Large Language Models (LLMs) from commercial and open-source providers
• Hands-on experience building Gen AI apps using LangChain, LangGraph, LlamaIndex, Hugging Face
• Proven design/implementation of Retrieval‑Augmented Generation (RAG) with vector databases
• Strong prompt engineering, workflow design, and Gen‑AI optimization skills
• Solid ML/DL foundation with PyTorch and/or TensorFlow, including embeddings and fine-tuning
• Experience deploying/operating production Gen AI systems with Docker and cloud-native architectures
• Strong Python software engineering skills, including FastAPI and asynchronous patterns
• Awareness of AI safety, governance, guardrails, and responsible AI concepts; capable of working in Agile environments

• communication
• collaboration
• problem solving
• LLMs (OpenAI, Gemini, Claude, Llama)
• LangChain, LangGraph, LlamaIndex, Hugging Face
• Retrieval-Augmented Generation (RAG)

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